コード例 #1
0
ファイル: KDTree.cs プロジェクト: htna/explsolv
        /**
         * Find KD-tree nodes whose keys are <I>n</I> nearest neighbors to
         * key. Uses algorithm above.  Neighbors are returned in ascending
         * order of distance to key.
         *
         * @param key key for KD-tree node
         * @param n how many neighbors to find
         *
         * @return objects at node nearest to key, or null on failure
         *
         * @throws KeySizeException if key.length mismatches K
         * @throws IllegalArgumentException if <I>n</I> is negative or
         * exceeds tree size
         */
        public TYPE[] nearest(double[] key, int n)
        {
            if (n < 0 || n > m_count)
            {
                throw new ArgumentException("Number of neighbors cannot be negative or greater than number of nodes");
            }

            if (key.Length != m_K)
            {
                throw new KeySizeException();
            }

            TYPE[] nbrs             = new TYPE[n];
            NearestNeighborList nnl = new NearestNeighborList(n);

            // initial call is with infinite hyper-rectangle and max distance
            HRect  hr           = HRect.infiniteHRect(key.Length);
            double max_dist_sqd = Double.MaxValue;
            HPoint keyp         = new HPoint(key);

            KDNode.nnbr(m_root, keyp, hr, max_dist_sqd, 0, m_K, nnl);

            for (int i = 0; i < n; ++i)
            {
                KDNode kd = (KDNode)nnl.removeHighest();
                nbrs[n - i - 1] = kd.v;
            }

            return(nbrs);
        }
コード例 #2
0
        // currently unused
        protected HRect intersection(HRect r)
        {
            HPoint newmin = new HPoint(min.coord.Length);
            HPoint newmax = new HPoint(min.coord.Length);

            for (int i = 0; i < min.coord.Length; ++i)
            {
                newmin.coord[i] = Math.Max(min.coord[i], r.min.coord[i]);
                newmax.coord[i] = Math.Min(max.coord[i], r.max.coord[i]);
                if (newmin.coord[i] >= newmax.coord[i])
                {
                    return(null);
                }
            }

            return(new HRect(newmin, newmax));
        }
コード例 #3
0
ファイル: KDTree.cs プロジェクト: htna/explsolv
 private static void hrcopy(HRect hr_src, HRect hr_dst)
 {
     hpcopy(hr_src.min, hr_dst.min);
     hpcopy(hr_src.max, hr_dst.max);
 }
コード例 #4
0
ファイル: KDTree.cs プロジェクト: htna/explsolv
            //static int _nnbr_stk = 0; // by htna to check stack overflow
            // Method Nearest Neighbor from Andrew Moore's thesis. Numbered
            // comments are direct quotes from there. Step "SDL" is added to
            // make the algorithm work correctly.  NearestNeighborList solution
            // courtesy of Bjoern Heckel.
            public static void nnbr(KDNode kd, HPoint target, HRect hr,
                                    double max_dist_sqd, int lev, int K,
                                    NearestNeighborList nnl)
            {
                //{	// by htna to check stack overflow
                //    _nnbr_stk++;
                //    if(_nnbr_stk >= 4000)
                //    {
                //        _nnbr_stk = 0;
                //        throw new StackOverflowException("stack overflow in KDTree.nnbr(...)");
                //    }
                //}

                // 1. if kd is empty then set dist-sqd to infinity and exit.
                if (kd == null)
                {
                    //_nnbr_stk--;
                    return;
                }

                // 2. s := split field of kd
                int s = lev % K;

                // 3. pivot := dom-elt field of kd
                HPoint pivot           = kd.k;
                double pivot_to_target = HPoint.sqrdist(pivot, target);

                // 4. Cut hr into to sub-hyperrectangles left-hr and right-hr.
                //    The cut plane is through pivot and perpendicular to the s
                //    dimension.
                HRect left_hr  = hr; // optimize by not cloning
                HRect right_hr = (HRect)hr.clone();

                left_hr.max.coord[s]  = pivot.coord[s];
                right_hr.min.coord[s] = pivot.coord[s];

                // 5. target-in-left := target_s <= pivot_s
                bool target_in_left = target.coord[s] < pivot.coord[s];

                KDNode nearer_kd;
                HRect  nearer_hr;
                KDNode further_kd;
                HRect  further_hr;

                // 6. if target-in-left then
                //    6.1. nearer-kd := left field of kd and nearer-hr := left-hr
                //    6.2. further-kd := right field of kd and further-hr := right-hr
                if (target_in_left)
                {
                    nearer_kd  = kd.left;
                    nearer_hr  = left_hr;
                    further_kd = kd.right;
                    further_hr = right_hr;
                }
                //
                // 7. if not target-in-left then
                //    7.1. nearer-kd := right field of kd and nearer-hr := right-hr
                //    7.2. further-kd := left field of kd and further-hr := left-hr
                else
                {
                    nearer_kd  = kd.right;
                    nearer_hr  = right_hr;
                    further_kd = kd.left;
                    further_hr = left_hr;
                }

                // 8. Recursively call Nearest Neighbor with paramters
                //    (nearer-kd, target, nearer-hr, max-dist-sqd), storing the
                //    results in nearest and dist-sqd
                nnbr(nearer_kd, target, nearer_hr, max_dist_sqd, lev + 1, K, nnl);

                KDNode nearest = (KDNode)nnl.getHighest();
                double dist_sqd;

                if (!nnl.isCapacityReached())
                {
                    dist_sqd = Double.MaxValue;
                }
                else
                {
                    dist_sqd = nnl.getMaxPriority();
                }

                // 9. max-dist-sqd := minimum of max-dist-sqd and dist-sqd
                max_dist_sqd = Math.Min(max_dist_sqd, dist_sqd);

                // 10. A nearer point could only lie in further-kd if there were some
                //     part of further-hr within distance sqrt(max-dist-sqd) of
                //     target.  If this is the case then
                HPoint closest = further_hr.closest(target);

                if (HPoint.eucdist(closest, target) < Math.Sqrt(max_dist_sqd))
                {
                    // 10.1 if (pivot-target)^2 < dist-sqd then
                    if (pivot_to_target < dist_sqd)
                    {
                        // 10.1.1 nearest := (pivot, range-elt field of kd)
                        nearest = kd;

                        // 10.1.2 dist-sqd = (pivot-target)^2
                        dist_sqd = pivot_to_target;

                        // add to nnl
                        if (!kd.deleted)
                        {
                            nnl.insert(kd, dist_sqd);
                        }

                        // 10.1.3 max-dist-sqd = dist-sqd
                        // max_dist_sqd = dist_sqd;
                        if (nnl.isCapacityReached())
                        {
                            max_dist_sqd = nnl.getMaxPriority();
                        }
                        else
                        {
                            max_dist_sqd = Double.MaxValue;
                        }
                    }

                    // 10.2 Recursively call Nearest Neighbor with parameters
                    //      (further-kd, target, further-hr, max-dist_sqd),
                    //      storing results in temp-nearest and temp-dist-sqd
                    nnbr(further_kd, target, further_hr, max_dist_sqd, lev + 1, K, nnl);
                    KDNode temp_nearest  = (KDNode)nnl.getHighest();
                    double temp_dist_sqd = nnl.getMaxPriority();

                    // 10.3 If tmp-dist-sqd < dist-sqd then
                    if (temp_dist_sqd < dist_sqd)
                    {
                        // 10.3.1 nearest := temp_nearest and dist_sqd := temp_dist_sqd
                        nearest  = temp_nearest;
                        dist_sqd = temp_dist_sqd;
                    }
                }

                // SDL: otherwise, current point is nearest
                else if (pivot_to_target < max_dist_sqd)
                {
                    nearest  = kd;
                    dist_sqd = pivot_to_target;
                }

                //{	// htna
                //    _nnbr_stk--;
                //}
            }